Answers to Your Forecasting Questions

The Journal of Business Forecasting, Fall 2007 | Go to article overview

Answers to Your Forecasting Questions


(ProQuest: ... denotes formula omitted.)

Q. Regarding the "Benchmarking Forecasting Errors" published in the Winter 06-07 issue of the Journal, are they goodness-of-fit (model fit) errors or did they come from the holdout periods? If it is not confidential, may I ask about the number of companies included in the analysis? Also, is it possible to see errors at other levels, if available-something with time dimension of week and month as well as category?

A. The errors we publish are the errors they actually experienced. They are not errors of holding periods as is defined in textbooks, but they are equivalent. The number of observations varies from question to question. For example, the number of people who responded to the forecast error of one-month ahead in 2006 is 98; 58 people responded to the forecast error of one-quarter ahead. In every IBF conference/tutorial, we ask attendees to respond to a number of questions. Everyone does not respond to every question. We have the forecast errors by one-month ahead, two-months ahead, one-quarter ahead, and one-year ahead. Also, the errors we publish are broken down by SKU, Category, and Aggregate. We don't publish errors experienced one-week ahead because we don't get much of a response to this question. It may be because fewer people prepare weekly forecast errors.

Q. What is the best forecasting model for Spare Parts Inventory Management?

A. For forecasting the demand for spare parts, both time series and regression models are used. If the demand of your product is intermittent, you may want to use the Croston model. To determine which one is the best, you may like to prepare ex post forecasts. In ex post forecasts we prepare forecasts with different models for those periods for which actuals are known, and then use the one that, on the average, yields the best forecasts. The periods for which we prepare forecasts are kept as holdout periods; as such, their data are not used in preparing forecasts.

Q. We are currently forecasting shipments, but my group wants to change to forecasting orders (demand). Our logistical group argues that since our case fill is already 99.2% with shipment forecasts, it is not necessary to change. My argument is that 99.2% number is just an average; it does not mean that every SKU has the same case fill. Therefore, we are not meeting all the demand. But if we start forecasting orders instead of shipments, it would help improve case fill and grow the sales. Furthermore, logistics can move shipment dates, whereas dates driving the forecast are fixed. Because of that order, forecasts of future months will be inaccurate and skewed. What is your opinion on that? …

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